What is the project, and why should you care? We are looking for a Senior ML Engineer to design, build, and optimize machine learning models and pipelines powering production systems. The ideal candidate brings deep hands-on experience across the ML lifecycle, with particular strength in recommender systems, deep learning, MLOps practices, and cloud-based ML infrastructure on AWS. You will be an excellent fit for this position if you have: * 4+ years of hands-on experience in machine learning engineering * Strong proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost, etc.). * Solid experience with deep learning — architecture design, training, hyperparameter tuning, and deployment of neural network models. * Proven experience designing and deploying recommender systems. * Hands-on experience with AWS SageMaker and broader AWS ML ecosystem. * Practical experience setting up data processing and ML workflows on AWS. * Strong MLOps skills. * Solid understanding of the full ML lifecycle. * Hands-on experience with containerization and orchestration in production environments. * Proficiency with SQL and experience working with both structured and unstructured data sources. * Strong problem-solving skills with an emphasis on scalability and performance optimization.
Here are some of the things you’ll be working on: * Design, train, and iterate on ML and deep learning models for recommendation, ranking, and personalization use cases. * Architect and maintain end-to-end ML pipelines on AWS. * Set up and optimize data processing and ML workflows using AWS services. * Build and maintain MLOps infrastructure. * Collaborate with data engineers to ensure data quality, build feature stores, and prepare datasets for model training and inference. * Evaluate and benchmark model performance, run offline and online experiments, and drive continuous improvement of model accuracy and efficiency. * Optimize model serving infrastructure for latency, throughput, and cost-effectiveness. * Partner with product and business stakeholders to translate requirements into well-scoped ML solutions. * Document model architecture, assumptions, performance characteristics, and known limitations. * Stay current with advances in recommendation systems, deep learning, and cloud ML services, and propose improvements to existing approaches.
